A method, device, equipment and storage medium for determining carbon emissions

Abstract

This invention discloses a method, apparatus, device, and storage medium for determining carbon emissions. The method includes: acquiring emission source information of an emission source to be determined; analyzing the composition of the emission source to be determined based on the emission source information using a preset large language model to output multiple emission source components; determining a carbon emission factor for each emission source component; and using the carbon emission factors to determine the carbon emissions of the emission source to be determined. The technical solution of this invention is applicable to estimating the carbon emissions of products, improving the accuracy of product carbon emission estimation.

Description

Technical Field

[0001] This invention relates to the field of carbon emission technology, and in particular to a method, apparatus, device, and storage medium for determining carbon emissions. Background Technology

[0002] Carbon footprint calculation and factor matching is a complex and multidimensional problem involving the assessment and management of greenhouse gas emissions directly or indirectly generated by a product throughout its life cycle. When conducting carbon footprint management, companies need to accurately collect and calculate carbon footprint data, which involves all stages of the product's life cycle, including raw material procurement, manufacturing, transportation, use, and waste disposal.

[0003] A carbon footprint is a measure of greenhouse gas emissions generated by an individual, organization, or product over its life cycle, expressed in carbon dioxide equivalents, and encompassing both direct and indirect emissions. A carbon emission factor quantifies the amount of carbon dioxide (CO2) or other greenhouse gases (GHG) emitted by a specific emission source per unit of activity. The carbon emission factor is the basic unit for calculating a carbon footprint; by multiplying activity data by the carbon emission factor, the carbon emissions of a specific activity can be derived. Carbon footprint databases provide systematic storage and management of carbon emission factors, ensuring data accuracy, consistency, and traceability, thereby improving the efficiency and reliability of carbon footprint accounting. In product carbon footprint accounting, the distinction between real-world data and background data is crucial. Real-world data is directly related to the product or service being evaluated, while background data is indirectly related and is typically obtained by accessing various public or commercial carbon footprint databases. The accuracy of the background data is critical to the accuracy of the final accounting results, which also involves the challenge of matching emission sources with single or multiple factors.

[0004] However, the accuracy of current estimates of carbon emission factors and carbon emissions for products, especially new products, is not good. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for determining carbon emissions, in order to solve the problem of poor accuracy in determining the carbon emissions of new products.

[0006] In a first aspect, the present invention provides a method for determining carbon emissions, comprising:

[0007] Obtain emission source information for the emission sources to be identified;

[0008] The composition of the emission source to be determined is analyzed based on the emission source information using a pre-defined large language model, so as to output multiple emission source components;

[0009] Determine the carbon emission factor for each of the emission source components, and use the carbon emission factor to determine the carbon emission amount of the emission source to be determined.

[0010] In a second aspect, the present invention provides an apparatus for determining carbon emissions, comprising:

[0011] The information acquisition module is used to acquire emission source information of the emission source to be determined;

[0012] The emission source composition determination module is used to analyze the composition of the emission source to be determined based on the emission source information using a preset large language model, so as to output multiple emission source components;

[0013] A carbon emission determination module is used to determine the carbon emission factor of each of the emission source components and to determine the carbon emission amount of the emission source to be determined using the carbon emission factor.

[0014] Thirdly, the present invention provides an electronic device comprising:

[0015] At least one processor;

[0016] and memory that is communicatively connected to at least one processor;

[0017] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the method for determining carbon emissions described in the first aspect above.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to execute the method for determining carbon emissions described in the first aspect.

[0019] The present invention provides a scheme for determining carbon emissions by acquiring emission source information of an emission source to be determined, analyzing the composition of the emission source based on the emission source information using a preset large language model to output multiple emission source components, determining the carbon emission factor of each emission source component, and using the carbon emission factor to determine the carbon emission amount of the emission source to be determined. By adopting the above technical solution, for emission sources with unknown carbon emissions, the preset large language model accurately analyzes the composition of the emission source, and uses the carbon emission factor of each component to achieve accurate estimation of the carbon emission amount of the emission source. This method is suitable for estimating the carbon emissions of products, improving the accuracy of carbon emission estimation for new products.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for determining carbon emissions according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for determining carbon emissions according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a flowchart of a method for determining carbon emissions according to Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of a device for determining carbon emissions according to Embodiment 4 of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. In the description of this invention, unless otherwise stated, "a plurality of" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0029] Example 1

[0030] Figure 1 The flowchart of a method for determining carbon emissions is provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of determining the carbon emissions of emission sources. The method can be executed by a device for determining carbon emissions. The device for determining carbon emissions can be implemented in hardware and / or software. The device for determining carbon emissions can be configured in an electronic device, which can be composed of two or more physical entities or a single physical entity.

[0031] like Figure 1 As shown, the method for determining carbon emissions provided in Embodiment 1 of the present invention specifically includes the following steps:

[0032] S101. Obtain emission source information for the emission source to be determined.

[0033] In this embodiment, emission source information of the emission source to be determined can be obtained in advance, such as emission source information of a product with unknown carbon emissions. The emission source information can include the name of the emission source, the emission unit, the applicable region, the product description, and the process description.

[0034] S102. Analyze the composition of the emission source to be determined based on the emission source information using a preset large language model, so as to output multiple emission source components.

[0035] In this embodiment, emission source information is input into a preset large language model, which can analyze the composition of the emission source to be determined and output multiple emission source components.

[0036] For example, if the emission source to be determined is corn milk, the preset large language model performs a compositional analysis on the corn milk, and the output emission source components include: corn, dairy products, concentrated protein, and sugarcane sugar.

[0037] S103. Determine the carbon emission factor for each of the emission source components, and use the carbon emission factor to determine the carbon emission amount of the emission source to be determined.

[0038] In this embodiment, the carbon emission factor of each emission source component can be determined separately, and the carbon emission amount of each emission source component can be determined using this carbon emission factor. The sum of the carbon emission amounts of these emission source components is the carbon emission amount of the emission source to be determined. If the carbon emission factor of the emission source component output by the preset large language model is unknown, and the emission source component can be further decomposed, the constituent components of the emission source component can be analyzed using the preset large language model, and then the carbon emission factors of the constituent components of the emission source component can be determined.

[0039] For example, if the emission source components include: corn, dairy products, concentrated protein, and cane sugar, and the proportions of each emission source component are 20%, 60%, 15%, and 5%, respectively, and the carbon emission factors of each emission source component are A, B, C, and D, then the method for determining the carbon emissions of the emission source to be determined can be expressed as follows:

[0040] Carbon emissions = (0.2*A + 0.6*B + 0.15*C + 0.05*D) * weight of emission source

[0041] The proportion of emission source components is the ratio of the weight of the emission source components to the weight of the emission source to be determined.

[0042] The process of determining the carbon footprint of an emission source after determining its carbon emissions can include the following eight steps:

[0043] 1) Carbon footprint application: Submit a carbon footprint accounting request for the product based on actual business needs, and receive the review results;

[0044] 2) Product entity confirmation: Create a new product carbon footprint accounting item according to the applicant's needs, select the product to be accounted for, and if there is no product information in the system, product information can be created;

[0045] 3) Time boundary confirmation: Select and confirm the time boundary information for product carbon footprint accounting;

[0046] 4) System Boundary Confirmation: Select the product carbon footprint accounting system boundary and fill in the product carbon footprint function / declaration unit. The system boundary includes: from cradle to gate and from cradle to grave. If the boundary needs to be updated, it can be updated and improved in the carbon footprint system boundary and method management.

[0047] 5) Confirm data source: After LCA (Life Cycle Assessment) modeling, confirm the data source of the product's carbon footprint based on the actual information of each stage of the product. The system supports data import and manual entry. If you choose to upload data, you need to upload the product's carbon footprint certificate and fill in the carbon emission values ​​for each stage.

[0048] 6) Data collection and data entry: Product carbon footprint accounting includes 5 stages: raw material acquisition stage, processing stage, distribution stage, installation and use stage and product disposal stage. For each stage, input and output data can be entered into the system through manual entry, data template import and manual entry, and the entered data can be summarized and confirmed.

[0049] 7) Carbon Footprint Methodology and Calculation: After data aggregation, select LCIA (Lean Continuous Improvement Approach), including IPCC, EF3.0, and CML. If the methodology needs updating, LCIA can be added or updated in the carbon footprint system boundary and methodology management. After LCIA confirmation, based on the selected evaluation methodology, the total carbon emissions will be automatically calculated from the carbon emissions database and input / output data, generating a carbon emissions register, carbon emissions report, and carbon emissions details. The calculated carbon footprint results will then be reviewed.

[0050] 8) Carbon Footprint Report Management: After reviewing and confirming the calculated product carbon footprint results, if external certification is required, a certification process can be initiated at the certification center. This involves selecting the required product carbon footprint calculation record and certification body, and uploading supporting documentation. The certification body will then return the certification certificate to the system after online certification on the platform. The system-generated carbon footprint report and the certification body's certification report can be managed.

[0051] The method for determining carbon emissions provided in this invention involves acquiring emission source information of an emission source to be determined, analyzing the composition of the emission source based on the emission source information using a preset large language model to output multiple emission source components, determining the carbon emission factor for each emission source component, and using the carbon emission factor to determine the carbon emission amount of the emission source to be determined. This technical solution of the present invention, for emission sources with unknown carbon emissions, accurately analyzes the composition of the emission source using a preset large language model, and uses the carbon emission factor of each component to achieve accurate estimation of the carbon emission amount of the emission source. It is applicable to estimating the carbon emissions of products, improving the accuracy of carbon emission estimation for new products.

[0052] Example 2

[0053] Figure 2This is a flowchart of a method for determining carbon emissions according to Embodiment 2 of the present invention. The technical solution of the present invention is further optimized based on the above optional technical solutions, and provides a specific way to determine the carbon emissions of emission sources.

[0054] Optionally, the emission source information includes the name and market launch time of the emission source to be determined. The step of analyzing the composition of the emission source to be determined using a preset large language model based on the emission source information to output multiple emission source components includes: if the market launch time is later than a preset time, obtaining multiple different first preset large language models; using each first preset large language model to analyze the emission source to be determined based on the name to obtain the composition of the emission source to be determined; if the composition output by each first preset large language model differs, filtering the total composition to obtain multiple emission source components of the emission source to be determined, wherein the total composition is the total amount of the components. The advantage of this setup is that for products with a relatively new market launch time, by using multiple different large language models to analyze the composition and filtering the determined components, the composition of the product can be accurately obtained.

[0055] Optionally, the preset large language model includes GPT.

[0056] Optionally, the preset large language model also outputs the proportion of the weight of each emission source component to the weight of the emission source to be determined; wherein, determining the carbon emission amount of the emission source to be determined using the carbon emission factor includes: determining the weight of each emission source component using the proportion, and determining the carbon emission amount of the emission source to be determined using the weight and the carbon emission factor.

[0057] like Figure 2 As shown in Embodiment 2 of the present invention, a method for determining carbon emissions specifically includes the following steps:

[0058] S201. Obtain emission source information of the emission source to be determined, wherein the emission source information includes the name and listing time of the emission source to be determined.

[0059] Specifically, the listing time can be the time of public sale, the time of public release, or the time of the most recent update, which represents the production time or the time when the composition of the emission source to be identified changes.

[0060] S202. Determine whether the listing time is later than the preset time. If yes, proceed to step S203; otherwise, proceed to step S208.

[0061] Specifically, if the listing time is later than the preset time, it means that the components of the emission source to be determined and the corresponding carbon emission factors may not be directly searchable.

[0062] S203, Obtain multiple different first GPTs.

[0063] Specifically, multiple (first) GPTs (Generative Pretrained Transformers) of different types or parameters can be obtained. GPT is a natural language processing technique based on pre-trained models.

[0064] S204. Analyze the emission source to be determined using each first GPT according to the name to obtain the composition of the emission source to be determined.

[0065] Specifically, each first GPT can output the composition of the emission source to be determined based on the name of the emission source to be determined.

[0066] S205. Determine whether the components of each first GPT output are consistent. If not, proceed to step S206; if yes, proceed to step S207.

[0067] Specifically, if the output (i.e. the components) of each first GPT are inconsistent and there are differences, further processing is required.

[0068] S206. Screen the total components to obtain multiple emission source components of the emission source to be determined.

[0069] Wherein, the total composition refers to the total amount of the constituent components.

[0070] Specifically, the overall composition can be deduplicated first to remove duplicate emission source components. Then, through manual review, it can be determined whether there are any unreasonable components in the deduplicated emission source components, so as to further screen the deduplicated emission sources and receive the results of the manual review. The results are the multiple emission source components of the emission source.

[0071] Furthermore, the step of screening the total components to obtain multiple emission source components of the emission source to be determined includes: performing clustering processing on the total components to obtain multiple clusters, and obtaining functional description information of the emission source to be determined, wherein each cluster contains at least one component; using a preset semantic recognition model to determine the emission source function of the emission source to be determined based on the functional description information; and screening the multiple clusters based on the emission source function to obtain multiple emission source components of the emission source to be determined.

[0072] For example, if the first GPT includes A, B, C, and D, and the components output by A include a and b, the components output by B include d and b', the components output by C include a and d', and the components output by D include a' and b, then the total components include a, a', b, b', d', and d. Clustering can be performed based on the names or semantics of the total components to obtain multiple clusters (a, a'), (b, b'), and (d, d'), and functional description information of the emission source to be determined can be obtained. Each cluster contains at least one component, and the functional description information explains the function of the emission source to be determined. This functional description information can then be input into a preset semantic recognition model, which can output the emission source function of the emission source to be determined. Clusters corresponding to the emission source function can be selected from the multiple clusters, i.e., component clusters associated with the emission source function can be selected. Then, the outputs in the clusters are deduplicated to obtain multiple emission source components of the emission source to be determined.

[0073] Furthermore, the step of filtering the multiple clusters based on the emission source function to obtain multiple emission source components of the emission source to be determined includes: matching the emission source function with the multiple clusters to obtain a matching result; if the matching result includes emission source functions that were not successfully matched, then using a second GPT to output candidate components based on the unmatched emission source functions; and performing deduplication processing on the components in the successfully matched clusters in the matching result and the candidate components to obtain the emission source components of the emission source to be determined.

[0074] Specifically, emission source functions can be matched with components in multiple clusters to obtain matching results. If the emission source function includes or matches the function corresponding to a component in a cluster, the emission source function is considered successfully matched. If the matching results include unmatched emission source functions, the content corresponding to the unmatched emission source functions is input into a second GPT to obtain new components, i.e., candidate components. Then, the components in the successfully matched clusters and the candidate components in the matching results are deduplicated to obtain the emission source components of the emission source to be determined. If the matching results do not include unmatched clusters or the matching results indicate that the emission source function and the cluster are successfully matched, the components in the clusters can be directly deduplicated, and the components obtained after deduplication are determined as the emission source components of the emission source to be determined.

[0075] S207. Determine the carbon emission factor of each of the emission source components; determine the weight of each of the emission source components using a ratio, and determine the carbon emission amount of the emission source to be determined using the weight and the carbon emission factor.

[0076] The GPT mentioned above also outputs the weight ratio of each emission source component to the weight of the emission source to be determined.

[0077] Specifically, the process of removing duplicate components to obtain emission source components, as described above, can be pre-processed to pre-calculate the proportions of duplicate components, such as by averaging or weighted averaging, to obtain the proportions of emission source components. For example, if the components in the first cluster include a (corresponding proportion of 5%) and b, and the components in the second cluster also include a (corresponding proportion of 6%) and c, then the components obtained after deduplication are a, b, and c, and the proportion corresponding to a can be (5% + 6%) / 2 = 5.5%. The weight of each emission source component can be determined first by multiplying it by its corresponding carbon emission factor, and the sum of these products can be used to determine the carbon emission amount of the emission source to be identified.

[0078] Furthermore, the method for determining the carbon emission factor of each emission source component includes: obtaining carbon emission correlation information for each emission source component, wherein the carbon emission correlation information is information affecting carbon emissions; and using a third GPT to output the carbon emission factor of each emission source component based on the carbon emission correlation information.

[0079] Specifically, if the carbon emission factor of the emission source component cannot be determined by searching the preset database, carbon emission correlation information for each emission source component can be obtained first, such as processing technology and working hours. Then, the carbon emission correlation information is input into (third) GPT, which can output the carbon emission factor for each emission source component. This carbon emission factor can be used as a standard value to be applied to other emission sources to be determined.

[0080] Optionally, the method for determining the carbon emission factor for each of the emission source components includes:

[0081] By searching the dataset (search criteria including name, unit, applicable region, product description, and process description, etc.), the icarbon standard factor or industry data factor (standard factor in the literature) is determined, and this factor is identified as the carbon emission factor of the emission source component.

[0082] in:

[0083] 1) You can first search for the names of the emission source components in the dataset.

[0084] 2) If multiple factors are found, and the units corresponding to these factors are different from the units of the emission source components (such as grams or kilograms), the units of the factors can be aligned with the units of the emission source components to obtain factors with units consistent with the emission source components.

[0085] 3) If multiple factors are found, but the production units corresponding to these factors are unknown, then a preset formula, such as: mass = density × volume, is used to perform unit conversion to obtain the carbon emission factors of the emission source components that are aligned with the units of the emission source to be determined.

[0086] 4) After obtaining factors with units consistent with the emission source components, if there are multiple factors, a pre-defined rule engine can be used for matching. Successfully matched factors are the carbon emission factors of the emission source components. For example, if the emission source component is corn and the factor unit is kilograms, the matching rules in the rule engine include:

[0087] The physical priority order can be: measured data > national data > Asian data > ROW (outside Europe) > international data (GLO);

[0088] Year of release: based on the nearest year, not exceeding 10 years;

[0089] Publishing agencies: Prioritize data released by authoritative international or national agencies, such as the United Nations IPCC and government departments in charge of emissions operations in various countries;

[0090] Scope of accounting: Select data lifecycle boundaries that are as complete as possible, with full lifecycle taking precedence over half lifecycle, and half lifecycle taking precedence over production process only.

[0091] 4) If no matching factor is found, keywords in the product description and / or process description of the emission source components can be extracted, and secondary searches can be performed using these keywords. A similarity algorithm can then be used to match factors to obtain the carbon emission factors of the emission source components.

[0092] S208. Analyze the emission source to be determined using the fourth GPT based on the name to obtain multiple emission source components of the emission source to be determined. Execute step S207.

[0093] Specifically, for undetermined emission sources whose listing time is equal to or later than the preset time, the components of the undetermined emission source and the corresponding carbon emission factors can generally be directly searched. The fourth GPT can be any one of the multiple different first GPTs mentioned above.

[0094] The method for determining carbon emissions provided in this invention provides an efficient way to determine the composition of a relatively new product by using multiple different GPT analyses and screening the identified components. Furthermore, by intelligently analyzing the proportion of components from emission sources and using this proportion along with the corresponding carbon emission factor, the carbon emissions can be quickly determined, thus improving efficiency.

[0095] Example 3

[0096] Figure 3 This is a flowchart of a method for determining carbon emissions according to Embodiment 3 of the present invention. The technical solution of the present invention is further optimized based on the above optional technical solutions, and provides another specific way to determine the carbon emissions of emission sources.

[0097] like Figure 3 As shown in Embodiment 3 of the present invention, a method for determining carbon emissions specifically includes the following steps:

[0098] S301. Obtain emission source information of the emission source to be determined, wherein the emission source information includes the name and listing time of the emission source to be determined.

[0099] S302. Determine whether the listing time is later than the preset time. If yes, proceed to step S303; otherwise, proceed to step S312.

[0100] S303, Obtain multiple different first GPTs.

[0101] S304. Analyze the emission source to be determined using each of the first GPTs according to the name to obtain the composition of the emission source to be determined.

[0102] S305. Determine whether the components of each first GPT output are consistent. If not, proceed to step S306; if yes, proceed to step S311.

[0103] S306. Perform clustering on the total components to obtain multiple clusters, and obtain the functional description information of the emission source to be determined.

[0104] S307. Determine the emission source function of the emission source to be determined based on the functional description information using a preset semantic recognition model.

[0105] S308. Match the emission source function with the multiple clusters to obtain the matching result.

[0106] S309. If the matching result includes emission source functions that were not successfully matched, then the second GPT is used to output candidate components based on the emission source functions that were not successfully matched.

[0107] S310. The components in the successfully matched clusters and the candidate components in the matching results are deduplicated to obtain the emission source components of the emission source to be determined.

[0108] S311. Determine the carbon emission factor of each of the emission source components; determine the weight of each of the emission source components using a ratio, and determine the carbon emission amount of the emission source to be determined using the weight and the carbon emission factor.

[0109] S312. Analyze the emission source to be determined using the fourth GPT based on the name to obtain multiple emission source components of the emission source to be determined. Execute step S311.

[0110] Specifically, for undetermined emission sources whose listing time is equal to or later than the preset time, the components of the undetermined emission source and the corresponding carbon emission factors can generally be directly searched.

[0111] Example 4

[0112] Figure 4 This is a schematic diagram of a device for determining carbon emissions according to Embodiment 4 of the present invention. Figure 4 As shown, the device includes: an information acquisition module 401, an emission source composition determination module 402, and a carbon emission determination module 403, wherein:

[0113] The information acquisition module is used to acquire emission source information of the emission source to be determined;

[0114] The emission source composition determination module is used to analyze the composition of the emission source to be determined based on the emission source information using a preset large language model, so as to output multiple emission source components;

[0115] A carbon emission determination module is used to determine the carbon emission factor of each of the emission source components and to determine the carbon emission amount of the emission source to be determined using the carbon emission factor.

[0116] The apparatus for determining carbon emissions provided in this invention accurately analyzes the components of an unknown carbon emission source using a preset large language model, and uses the carbon emission factor of each component to accurately estimate the carbon emissions of the emission source. It is suitable for estimating the carbon emissions of products and improves the accuracy of estimating the carbon emissions of new products.

[0117] Optionally, the emission source information includes the name and listing time of the emission source to be determined.

[0118] Optionally, the emission source composition determination module includes:

[0119] The model acquisition unit is used to acquire multiple different first preset large language models if the listing time is later than a preset time.

[0120] An emission source analysis unit is used to analyze the emission source to be determined based on the name using each of the first preset large language models, so as to obtain the composition of the emission source to be determined;

[0121] The emission source component determination unit is used to filter the total component if there are differences in the component output by each of the first preset large language models, so as to obtain multiple emission source components of the emission source to be determined, wherein the total component is the total amount of the component.

[0122] Furthermore, the step of screening the total components to obtain multiple emission source components of the emission source to be determined includes: performing clustering processing on the total components to obtain multiple clusters, and obtaining functional description information of the emission source to be determined, wherein each cluster contains at least one component; using a preset semantic recognition model to determine the emission source function of the emission source to be determined based on the functional description information; and screening the multiple clusters based on the emission source function to obtain multiple emission source components of the emission source to be determined.

[0123] Furthermore, the step of filtering the multiple clusters based on the emission source function to obtain multiple emission source components of the emission source to be determined includes: matching the emission source function with the multiple clusters to obtain a matching result; if the matching result includes emission source functions that were not successfully matched, then using a second preset large language model to output candidate components based on the unmatched emission source functions; and performing deduplication processing on the components in the successfully matched clusters in the matching result and the candidate components to obtain the emission source components of the emission source to be determined.

[0124] Optionally, the method for determining the carbon emission factor of each emission source component includes: obtaining carbon emission correlation information for each emission source component, wherein the carbon emission correlation information is information affecting carbon emissions; and using a third preset large language model to output the carbon emission factor of each emission source component based on the carbon emission correlation information.

[0125] Optionally, the preset large language model includes GPT.

[0126] Optionally, the preset large language model also outputs the proportion of the weight of each emission source component to the weight of the emission source to be determined.

[0127] Optional, the carbon emissions determination module includes:

[0128] A carbon emission determination unit is used to determine the weight of each of the emission source components using the ratio, and to determine the carbon emission amount of the emission source to be determined using the weight and the carbon emission factor.

[0129] The apparatus for determining carbon emissions provided in the embodiments of the present invention can execute the method for determining carbon emissions provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0130] Example 5

[0131] Figure 5 A schematic diagram of an electronic device 50 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0132] like Figure 5 As shown, the electronic device 50 includes at least one processor 51 and a memory, such as a read-only memory (ROM) 52 and a random access memory (RAM) 53, communicatively connected to the at least one processor 51. The memory stores computer programs executable by the at least one processor. The processor 51 can perform various appropriate actions and processes based on the computer program stored in the ROM 52 or loaded into the RAM 53 from storage unit 58. The RAM 53 can also store various programs and data required for the operation of the electronic device 50. The processor 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0133] Multiple components in electronic device 50 are connected to I / O interface 55, including: input unit 56, such as keyboard, mouse, etc.; output unit 57, such as various types of monitors, speakers, etc.; storage unit 58, such as disk, optical disk, etc.; and communication unit 59, such as network card, modem, wireless transceiver, etc. Communication unit 59 allows electronic device 50 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0134] Processor 51 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 51 performs the various methods and processes described above, such as methods for determining carbon emissions.

[0135] In some embodiments, the method for determining carbon emissions may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 50 via ROM 52 and / or communication unit 59. When the computer program is loaded into RAM 53 and executed by processor 51, one or more steps of the method for determining carbon emissions described above may be performed. Alternatively, in other embodiments, processor 51 may be configured to perform the method for determining carbon emissions by any other suitable means (e.g., by means of firmware).

[0136] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0138] The computer equipment provided above can be used to execute the method for determining carbon emissions provided in any of the above embodiments, and has the corresponding functions and beneficial effects.

[0139] Example 6

[0140] In the context of this invention, a computer-readable storage medium may be a tangible medium, and the computer-executable instructions, when executed by a computer processor, are used to perform a method for determining carbon emissions, the method comprising:

[0141] Obtain emission source information for the emission sources to be identified;

[0142] The composition of the emission source to be determined is analyzed based on the emission source information using a pre-defined large language model, so as to output multiple emission source components;

[0143] Determine the carbon emission factor for each of the emission source components, and use the carbon emission factor to determine the carbon emission amount of the emission source to be determined.

[0144] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by, or in conjunction with, an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0145] The computer equipment provided above can be used to execute the method for determining carbon emissions provided in any of the above embodiments, and has the corresponding functions and beneficial effects.

[0146] It is worth noting that in the embodiments of the above-mentioned device for determining carbon emissions, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0147] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for determining carbon emissions, characterized in that, include: Obtain emission source information for the emission sources to be identified; The composition of the emission source to be determined is analyzed based on the emission source information using a pre-defined large language model, so as to output multiple emission source components; Determine the carbon emission factor for each of the emission source components, and use the carbon emission factor to determine the carbon emission amount of the emission source to be determined.

2. The method according to claim 1, characterized in that, The emission source information includes the name and listing time of the emission source to be determined; wherein, the step of using a preset large language model to analyze the composition of the emission source to be determined based on the emission source information to output multiple emission source components, including: If the launch time is later than the preset time, then multiple different first preset large language models are obtained; The emission source to be determined is analyzed based on the name using each of the first preset large language models to obtain the composition of the emission source to be determined; If the components output by each of the first preset large language models are different, the total components are filtered to obtain multiple emission source components of the emission source to be determined, wherein the total components are the total amount of the components.

3. The method according to claim 2, characterized in that, The process of screening the total components to obtain multiple emission source components of the emission source to be determined includes: The total components are clustered to obtain multiple clusters, and the functional description information of the emission source to be determined is obtained, wherein each cluster contains at least one component. The emission source function of the emission source to be determined is determined based on the functional description information using a preset semantic recognition model; The multiple clusters are screened according to the emission source function to obtain multiple emission source components of the emission source to be determined.

4. The method according to claim 3, characterized in that, The step of filtering the multiple clusters based on the emission source function to obtain multiple emission source components of the emission source to be determined includes: The emission source functions are matched with the multiple clusters to obtain matching results; If the matching result includes unmatched emission source functions, then the second preset large language model is used to output candidate components based on the unmatched emission source functions; The components of the successfully matched clusters and the candidate components in the matching results are deduplicated to obtain the emission source components of the emission source to be determined.

5. The method according to claim 1, characterized in that, Determining the carbon emission factor for each of the emission source components includes: Obtain carbon emission correlation information for each of the emission source components, wherein the carbon emission correlation information is information affecting carbon emissions; The carbon emission factor of each emission source component is output based on the carbon emission correlation information using a third preset large language model.

6. The method according to any one of claims 1-5, characterized in that, The preset large language model includes GPT.

7. The method according to claim 1, characterized in that, The preset large language model also outputs the proportion of the weight of each emission source component to the weight of the emission source to be determined; wherein, determining the carbon emission amount of the emission source to be determined using the carbon emission factor includes: The weight of each of the emission source components is determined using the ratio, and the carbon emissions of the emission source to be determined are determined using the weight and the carbon emission factor.

8. A device for determining carbon emissions, characterized in that, include: The information acquisition module is used to acquire emission source information of the emission source to be determined; The emission source composition determination module is used to analyze the composition of the emission source to be determined based on the emission source information using a preset large language model, so as to output multiple emission source components; A carbon emission determination module is used to determine the carbon emission factor of each of the emission source components and to determine the carbon emission amount of the emission source to be determined using the carbon emission factor.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method for determining carbon emissions as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining carbon emissions as described in any one of claims 1-7.

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